Chasing the Tail: Effective Rubric-based Reward Modeling for Large Language Model Post-Training

This paper addresses reward over-optimization in Large Language Model (LLM) training, where models exploit proxy rewards to achieve high scores without actually improving quality. It theoretically and empirically demonstrates that accurately distinguishing between excellent and merely great LLM responses (the "high-reward tail") is crucial. The authors propose and validate an iterative rubric refinement method, using off-policy LLM responses to generate more precise evaluation criteria, significantly mitigating over-optimization and improving LLM alignment.

Several authors are affiliated with Scale AI, Inc., and some work was conducted during internships at Scale AI. Scale AI is a company involved in AI data annotation and model evaluation, making their business directly aligned with the paper's goal of improving LLM reward modeling and alignment. This constitutes a direct conflict of interest.

This paper makes significant contributions to LLM alignment by providing strong theoretical and empirical evidence for a novel method to combat reward over-optimization. The iterative rubric refinement approach is well-designed and shown to be effective across different domains. While the reliance on LLMs for evaluation is a noted dependency, the authors' methodology is sound within the current context of LLM research. The identified conflict of interest, while present, does not diminish the scientific merit of the work itself.